class_docstr: Huber loss.
  
  Args:
      beta (float, optional): The threshold in the piecewise function.
          Defaults to 1.0.
      reduction (str, optional): The method to reduce the loss.
          Options are "none", "mean" and "sum". Defaults to "mean".
      loss_weight (float, optional): The weight of loss.
code: def __init__(
      self,
      beta: float = 1.0,
      reduction: str = "mean",
      loss_weight: float = 1.0,
  ) -> None:
      super().__init__()
      self.beta = beta
      self.reduction = reduction
      self.loss_weight = loss_weight
  
file_path: mmdetection/projects/EfficientDet/efficientdet/huber_loss.py
imports: from typing import Optional
  import torch
  import torch.nn
  from torch import Tensor
  from mmdet.models.losses.utils import weighted_loss
  from mmdet.registry import MODELS
label_desc: Use image node_id 1 to create a new HuberLoss object from inherited base classes: nn with example: obj = HuberLoss(beta, reduction, loss_weight)